Diagnosis of asthma versus chronic obstructive pulmonary disease (COPD) using urine metabolomic analysis

ABSTRACT

Described are methods and uses employing metabolomic data to diagnose an asthma disease state or a Chronic Obstructive Pulmonary Disease (COPD) state. Further described are methods of uses of employing metabolomic data to distinguish between asthma and COPD. In particular, urinary metabolomic profiles are employed to enable differential diagnosis of asthma and COPD.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/579,524, filed Dec. 4, 2017, now U.S. Pat. No. 10,791,960, which is anational stage application under 35 U.S.C. 371 of PCT Application NO.PCT/CA2016/050637, having an international filing date of Jun. 3, 2016,which designated the United States, which PCT application claimed thebenefit of U.S. provisional patent application Ser. No. 62/170,848 filedJun. 4, 2015, each of which are hereby incorporated by reference hereinin their entirety.

TECHNICAL FIELD

The present application relates to diagnosis of pulmonary disorders, andmore particularly, to improved diagnosis of asthma versus chronicobstructive pulmonary disease (COPD) using metabolomic analysis.

BACKGROUND

Diseases of the respiratory system are among the leading diseases interms of their impact on society. According to the 1998/99 NationalPopulation Health Survey, there were around 2,474,400 Canadiansdiagnosed with asthma and 498,900 with COPD. These illnesses resulted in454 and 9,398 deaths, respectively. COPD is the 5th leading cause ofdeath in Canada, and the only one that is increasing in prevalence. Theprevalence of asthma is increasing worldwide, and it is the most commonchronic disease of childhood. These conditions place a significantburden on the healthcare system, accounting for over $4 billion annuallyin direct and indirect costs.

A feature common to most diseases of the respiratory system is some formof lung inflammation. Lung inflammation consists of specificinflammatory cells and by products generated by cellular activity. Thus,specific lung diseases are often diagnosed not only by their clinicalpresentation, but also by the type of inflammation measured.Inflammatory cells release enzymes and other proteins in the airway,which can be measured and are specific to the cell type (i.e. mast celltryptase or eosinophil cationic protein). The pathology of asthma isdifferent from that seen in COPD, and the degree of inflammation andcellular damage varies with disease severity. For example, patients withasthma often have sputum samples positive for cells called eosinophils,and those with COPD or pneumonia present with increased sputumneutrophils. Differentiating asthma from other causes of chronic airflowlimitation such as Chronic Obstructive Pulmonary Disease (COPD) can bedifficult in a typical outpatient setting.

In addition, the degree of inflammation and cellular damage varies withasthma or COPD severity.

The treatments for each disease are designed to address thisinflammation (i.e. corticosteroids or biologics versus antibiotics).

Unfortunately for clinicians, detecting this inflammation in individualpatients is often difficult. Instead, clinicians rely on physiological(i.e. spirometry, peak flow, airway hyperreactivity (AHR)) or functionalmeasurements (i.e. symptoms, or quality of life) to assess response totherapy. These tests, while useful, appear to be somewhat insensitive tochanges in inflammatory status that later become clinically relevant.

While accurate airway inflammation measurements from bronchoscopy arepossible, it is invasive and unavailable in the daily clinical setting.Thus, research has focused on non-invasive measures of inflammation suchas induced sputum, eNO, and various inflammatory markers in body fluids.While experience with sputum has shown valuable results, significantbarriers remain to its use clinically, including limited availability inmost centers and the inability of young children and even many adults toexpectorate. Exhaled NO (eNO) overcomes some of these barriers; however,while eNO shows correlation with asthma inflammation and outcomesexperimentally, it requires time, co-operation, age greater than 4 yearsand, unless very carefully performed, may measure sinus rather thanairway values. Ultimately, eNO still lacks the sensitivity andspecificity of induced sputum. While other tests for asthma inflammationin blood or urine have been studied, i.e. urine leukotrienes, oreosinophil protein X, they lack the sensitivity required for clinicalpractice. Overall, a simple, non-invasive, readily available andsensible test for patients with airway inflammation is currently notwidely available.

Using objective measurements of airway inflammation to guide asthmatherapy can produce superior therapeutic results compared withtraditional measures alone (e.g., symptoms and lung function).

For clinicians in a typical outpatient setting, detecting airwayinflammation is often difficult. As such, differentiating asthma fromother respiratory diseases like Chronic Obstructive Pulmonary Disease(COPD) can be difficult as both have somewhat similar clinicalpresentations. Objective measurements for differentiation of asthma andCOPD are not used in a typical doctor's office. Most clinicians continueto rely upon patient history and examination before making a diagnosisand administering trials of therapy. If the incorrect diagnosis is made,then the prescribed treatment will likely be ineffective, and exposingpatients to un-necessary side-effects and the patients respiratoryproblem will likely persist costing the health care system money andcausing longer suffering by the patient.

Metabolomics is the study of metabolic pathways and biochemicalmolecules created in a living system. By measuring changes inmetabolites, biochemical effects induced by a disease or its therapeuticintervention can be determined. However, typically it is not just achange in one metabolite that allows for identification of a diseasestate or the effects of therapeutic intervention. Rather, a combinationof metabolites must be identified and analysed in order for relevantinformation to be obtained.

Accordingly, there remains a need to provide a reliable and easy-to-usemethod of objective measurements for improved diagnosis of asthma versuschronic obstructive pulmonary disease (COPD) that can overcome thelimitations of the prior art.

SUMMARY

A metabolomic approach for improved diagnosis of asthma versus chronicobstructive pulmonary disease (COPD) is provided. Multivariatestatistical analysis of data from urine can identify distinctmetabolomic profiles that correlate with the clinical phenotypes ofasthma versus COPD.

Analysis of metabolites in urine, as provided herein, can have theability differentiate patients with asthma versus COPD both in theEmergency Department (ED) at the time of exacerbation and in follow-uppost treatment.

Broadly stated, in some embodiments, a method is provided for diagnosingan asthma disease state in a subject comprising: obtaining metabolomicdata on an obtained biological sample from the subject to obtain asubject profile; b. performing a statistical analysis on the metabolomicdata to compare the subject profile to a predetermined asthma diseasestate profile and to a predetermined Chronic Obstructive PulmonaryDisease (COPD) disease state profile, wherein the analysis does notcomprise identification of components of the biological sample; and c.providing a diagnosis of the asthma disease state when the statisticalanalysis identifies the subject profile as more similar to thepredetermined asthma disease state profile than to the predeterminedCOPD disease state profile.

Broadly stated, in some embodiments, a method is provided for diagnosingan asthma disease state in a subject comprising: measuring aconcentration of at least 11 of the metabolites as disclosed herein inan obtained biological sample from the subject to determine a subjectprofile; comparing the subject profile, by a statistical analysis, to apredetermined asthma disease state profile and to a predeterminedChronic Obstructive Pulmonary Disease (COPD) disease state profile; andproviding a diagnosis of the asthma disease state when the statisticalanalysis identifies the subject profile as more similar to thepredetermined asthma disease state profile than to the predeterminedCOPD disease state profile.

Broadly stated, in some embodiments, a method is provided for diagnosinga Chronic Obstructive Pulmonary Disease (COPD) disease state in asubject comprising: a. obtaining metabolomic data on an obtainedbiological sample from the subject to obtain a subject profile; b.performing a statistical analysis on the metabolomic data to compare thesubject profile to a predetermined asthma disease state profile and to apredetermined Chronic Obstructive Pulmonary Disease (COPD) disease stateprofile, wherein the analysis does not comprise identification ofcomponents of the biological sample; and c. providing a diagnosis of theCOPD disease state when the statistical analysis identifies the subjectprofile as more similar to the predetermined COPD disease state profilethan to the predetermined asthma disease state profile.

Broadly stated, in some embodiments, a method is provided for diagnosinga Chronic Obstructive Pulmonary Disease (COPD) disease state in asubject comprising: a. measuring a concentration of at least 11 of themetabolites as disclosed herein in an obtained biological sample fromthe subject to determine a subject profile; b. comparing the subjectprofile, by a statistical analysis, to a predetermined asthma diseasestate profile and to a predetermined Chronic Obstructive PulmonaryDisease (COPD) disease state profile; and c. providing a diagnosis ofthe COPD disease state when the statistical analysis identifies thesubject profile as more similar to the predetermined COPD disease stateprofile than to the predetermined asthma disease state profile.

Broadly stated, in some embodiments, a method is provided for creating apredetermined profile for differentiating between an asthma diseasestate and a Chronic Obstructive Pulmonary Disease (COPD) disease statein a subject comprising: measuring the concentration of at least 11 ofthe metabolites as disclosed herein in each of a plurality of urinesamples obtained from asthma patients and COPD patients; performing astatistical analysis on the concentration values of (a) to differentiatebetween the asthma disease state and the COPD disease state.

Broadly stated, in some embodiments, a diagnostic model is provided fordifferentiating between an asthma disease state and a ChronicObstructive Pulmonary Disease (COPD) disease state in a subject, thediagnostic model comprising at least 11 of the metabolites as disclosedherein.

Broadly stated, in some embodiments, a use of a diagnostic model asdescribed herein is provided for diagnosing an asthma disease stateand/or a COPD disease state in a subject in a subject.

In some embodiments, a computer readable medium is provided, thecomputer readable medium comprising instructions for carrying out themethods and/or the uses according to the methods and/or the uses asdescribed herein.

In some embodiments the biological sample is urine.

In some embodiments the statistical analysis id a partial least squaresdiscriminant analysis.

In some embodiments the concentration of at least one metabolite isdetermined using one or more or a combination of spectrometric andspectroscopic techniques selected from the group consisting of liquidchromatography, gas chromatography, high performance liquidchromatography, capillary electrophoresis, mass spectrometry, liquidchromatography-mass spectrometry, gas chromatography-mass spectrometry,high performance liquid chromatography-mass spectrometry, capillaryelectrophoresis-mass spectrometry, raman spectroscopy, near infraredspectroscopy, and nuclear magnetic resonance spectroscopy.

In some embodiments a method of quantifying at least one metabolite witha phenol or amine functional group is provided comprising preparing aurine sample from a patient comprising, diluting the sample with ACN,buffer and ¹²C-DNS-Cl; heating the sample; adding NaOH to the sample;further heating the sample; acidifying the sample; increasing the volumeof the sample; spiking the sample with ¹³C-IS; and increasing the volumeof the sample with 50% ACN; running the prepared urine sample throughthe mass spectrometer to obtain a measurement of the at least onemetabolite with a phenol or amine functional group; determining theconcentration of creatinine in the sample; and normalizing themeasurement of the at least one metabolite with a phenol or aminefunctional group using the concentration of creatinine in the sample.

In some embodiments a method of quantifying at least one metabolite witha carboxylic acid functional group is provided, the method comprisingpreparing a urine sample from a patient comprising, diluting the samplewith ACN; combining the sample with TEA and ¹²C-DmPA; heating thesample; cooling the sample to room temperature; and diluting the samplewith formic acid, ACN and ¹³C-DmPA standard solution; running theprepared urine sample through the mass spectrometer to obtain ameasurement of the at least one metabolite with a carboxylic acidfunctional group; determining the concentration of creatinine in thesample; and normalizing the measurement of the at least one metabolitewith a carboxylic acid functional group using the concentration ofcreatinine in the sample.

In some embodiments a method of diagnosing asthma versus COPD isprovided comprising obtaining a biological sample from a patient;identifying the concentration of metabolites present in the biologicalsample using the method of either one or both of the quantificationmethods described above to create a subject profile; comparing thesubject profile, by a statistical analysis, to a predetermined asthmadisease state profile and to a predetermined Chronic ObstructivePulmonary Disease (COPD) disease state profile; and providing adiagnosis of asthma or COPD when the statistical analysis identifies thesubject profile as more similar to one of the predetermined diseasestate profiles than to the other predetermined disease state profile.

In some embodiments the metabolomic data includes information on atleast 11 metabolites.

In some embodiments the at least 11 metabolites include3-Hydroxyisovalerate, Glutamine, Arginine, Lactic acid, Glycolate,Tyrosine, 2-oxoglutarate, Glycine, Histidine, 1-methylhistamine andTaurine.

In some embodiments the at least 11 metabolites further include anynumber of the VIP metabolites.

In some embodiments the method may further include administering atreatment for asthma or COPD to the subject, as appropriate based on thediagnosis. In some embodiments the treatment for asthma comprises one ormore of high dose corticosteroids and biological therapy targetingasthmatic inflammation. In some embodiments the treatment for COPDcomprises one or more of anti-muscarinic inhaler, antibiotics, lungsurgery and lung transplantation.

In some embodiments the step of obtaining metabolomic data is performedusing the method of either one or both of the quantification methodsdescribed above.

In some embodiments the step of measuring the concentration of at least11 metabolites is performed using the method of either one or both ofthe quantification methods described above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C depict an embodiment of a metabolomic model of exacerbationof asthma versus COPD in the Emergency Department (ED).

FIGS. 2A-2C depict an embodiment of a metabolomic model of subjects infollow-up post-exacerbation of asthma versus COPD.

FIGS. 3A-3B depict an embodiment of a metabolomic model correctlypredicting asthma versus COPD in blinded outpatients.

FIGS. 4A-4C depict representative LC-MS/MS spectra data of phenol oramine functional groups tested for FIG. 4A standard solution; FIG. 4Burine of asthma patient; and FIG. 4C urine of COPD patient;

FIGS. 5A-5C depict representative LC-MS/MS spectra data of carboxylicacid functional groups tested for FIG. 5A standard solution; FIG. 5Burine of asthma patient; and FIG. 5C urine of COPD patient;

FIG. 6 depicts the PLSDA results of MS data; and

FIGS. 7A (VIP) and 7B (COV) depict the differences in metabolitesbetween asthma and COPD patients outlined in Example 5.

DETAILED DESCRIPTION OF EMBODIMENTS

Metabolomics is the study of metabolic pathways and biochemicalmolecules created in a living system. By measuring changes inmetabolites, biochemical effects induced by a disease or its therapeuticintervention can be determined. Methods of identifying and quantifyingmetabolites can include spectrometric and spectroscopic techniquesincluding but not limited to liquid chromatography, gas chromatography,high performance liquid chromatography, capillary electrophoresis, massspectrometry, liquid chromatography-mass spectrometry, gaschromatography-mass spectrometry, high performance liquidchromatography-mass spectrometry, capillary electrophoresis-massspectrometry, raman spectroscopy, near infrared spectroscopy, andnuclear magnetic resonance spectroscopy.

1H-nuclear magnetic resonance (NMR) spectroscopy, for example, can beused to quantify metabolites within a biofluid. NMR is a usefultechnology because it provides both qualitative and quantitativemeasurements, while simultaneously studying a number of compounds in thesame biologic fluid. Once a metabolite has been identified, othertechniques such as mass spectrometry could be used to quantifymetabolites. Urine is a primary candidate biological fluid owing to itsease of collection in patients of all ages, low cell and proteincontent, and rich chemical composition.

Mass Spectrometry (MS) is another technology which has the ability tomeasures thousands more metabolites than NMR, but its methodology makesscreening more difficult. As discussed below, for absolutequantification of metabolites, MS requires specific methods designed foreach structurally-related metabolite. Once a set of candidate biomarkersis proposed, it is necessary to obtain isotope labeled standards toconfirm metabolite identity, and then develop calibration curves usingnovel methods in order to quantify concentrations. This requires animmense of amount of work. Therefore, it is preferred to initiallyidentify metabolites using NMR and then confirm their presence andquantification using MS.

COPD and asthma involve a number of unique cellular pathways, but thesepathways can still converge to produce a similar clinical outcome.Symptoms or lung function measurements may not differentiate asthma fromCOPD (e.g. airway remodeling in asthma gives a pattern of fixedobstruction). Thus, a unique combination of metabolites, rather than asingle metabolite, can be required to differentiate these complexpathologies. Using only one factor to separate groups requires largeconsistent differences with minimal measurement overlap. This has notbeen demonstrated for previous single biomarkers (e.g. eNO, urineleukotrienes, eosinophil cationic protein). Airway dysfunction,increased work of breathing, and hypoxemia will cause cellular stressinside and outside the lung. Using urine as a sample biofluid can beless invasive than samples from the airway. The ability to use urinemakes the methodology advantageously applicable to most clinicalsettings, especially for children.

The metabolic activity of patients with asthma would be expected todiffer from those with COPD. As a result, the profile of metabolitespresent in a biological sample from a patient with asthma would beexpected to differ from the profile of metabolites present in abiological sample from a patient with COPD. Specific methods andprocesses employed to identify and quantify particular metabolitesand/or metabolite profiles that are relevant to asthma and COPD havebeen developed and improved are disclosed herein.

Differentiating asthma from other causes of chronic airflow limitationsuch as Chronic Obstructive Pulmonary Disease (COPD) is difficult inoutpatient settings. The inflammation of asthma typically is differentcompared to COPD, and the degree of inflammation and cellular damagevaries with asthma severity. Therefore, the diagnosis and treatmentsprescribed differ in important ways.

In general terms, the diagnosis of asthma, COPD and/or theidentification of asthma versus COPD can be performed using thefollowing steps. A set of metabolomic data is obtained on a biologicalsample from a subject in order to obtain a subject profile. Statisticalanalysis can then be performed on the metabolomics data which comparesthe subject profile to predetermined metabolomic models representingeither the asthma or COPD disease state. Based on the results of thestatistical analysis, a diagnosis of a disease state can be madepossible.

Table 1 shows the metabolites which have been identified as andVariables of Importance Plot (VIP) metabolites and which can be used inany number of combinations to create a metabolomic model.

TABLE 1 VIP metabolites 1 Pantothenic acid 2 Cis/Trans aconitic acids 33 Hydroxy 3 Methyl glutaric acid 4 Lactic acid 5 Ethanolamine 6Pyroglutamic acid 7 Glycolic acid 8 Tryptophane 9 Creatinine 102-oxoglutaric acid 11 Valine 12 2-hydroxy isobutyric acid 13 1-methylhistamine 14 Asparagine 15 Lysine 16 O-acetylcarnitine 17 Serine 18Alanine 19 Isoleucine 20 3-methyl adipic acid 21 Tyrosine 22 3-hydroxybutyric acid 23 Succinic acid 24 Glycine 25 Sarcosine 26 Histidine 27Betaine 28 Threonine 29 Taurine 30 3-hydroxy isovaleric acid 31Glutamine

The number of metabolites chosen in the Examples below was based on datafrom a cohort of subjects and describes the preferred embodiment, sincethe sensitivity and specificity were optimized with this number ofmetabolites. However, there is evidence that with the removal of some ofthe metabolites from the model reasonable accuracy is maintained. Inaddition, the sample size of subjects outlined in the Examples, whilereasonable, is still relatively small when compared to some largerstudies. It would be anticipated that as the sample size increases andadditional data from the urine analysis using mass spectrometry (MS) isamassed to create the predetermined disease state profiles, some of thelisted metabolites will not be required for appropriate diagnosis. Thisis supported by the MS data disclosed herein, where reasonablediagnostic accuracy (R2 0.8 Q2 0.5) can be seen with the use of 14metabolites (see FIGS. 6 and 7A-7B). Using the MS method described belowthe number of metabolites used for analysis can be decreased to 11. Withthis number of metabolites each of the subjects remain correctly intheir diagnostic grouping, with the exception of one COPD subject. Assuch, it is reasonable that the methods described herein could be adiagnostic with as few as 10 of the listed metabolites. In a preferredembodiment this list of 11 metabolites would include3-Hydroxyisovalerate, Glutamine, Arginine, Lactic acid, Glycolate,Tyrosine, 2-oxoglutarate, Glycine, Histidine, 1-methylhistamine andTaurine.

More specifically, in order to obtain the metabolomic data from a samplefrom a subject in order to identify a subject profile, a quantitativeanalysis of more than one metabolite of interest can occur within theclinicians' office or associated lab. This analysis would include twoseparate runs through a mass spectrometer machine.

By way of general overview, the subject provides a urine sample, which,unlike urine samples collected for other tests, must be saved in such away as to prevent bacterial growth from occurring, because bacterialgrowth alters the metabolomics data. One method which has been developedinvolves cooling and quickly freezing the urine sample within an hour ofcollection, which preserves the metabolomics data. Repeated thawing andfreezing cycles also has been shown to alter the metabolomics data.Therefore, in the preferred embodiment the urine sample is collectedfrom the patient and frozen and not thawed until it is used for NMR orMass Spectrometry analysis.

It is also contemplated that sodium azide could be used to preventbacterial growth, however if this method is employed the samples must bestored at minus 80° C.

As disclosed herein and outlined below, it is possible to perform a massspectrometer analysis for at least 38 metabolites on a single run. In apreferred embodiment 38 metabolites were measured on a single run. Oncethe metabolomics data has been collected, statistical analysis isperformed on the data. In a preferred embodiment, data are analyzed bythe PLS-DA models, which can represent various clinical states of asthmaor COPD. The physician requesting the analysis can then receive a scorefor the likelihood that the subject has a predetermined clinical state(e.g. COPD or asthma).

This method can be important in the clinical setting because it canimprove the likelihood of obtaining a proper diagnosis of asthma versusCOPD, without using invasive techniques, which can lead to moreappropriate treatment of the patient and reduce costs within a healthcare system.

In terms of treatments, after a diagnosis of asthma, treatment mayinclude high dose corticosteroids (inhaled or systemic) and/or newbiological therapies that target asthmatic inflammation (e.g. anti-IL5,IgE, or IL13). These therapies would not be effective in COPD andobtaining a more definitive diagnosis of asthma as opposed to COPD couldavoid unnecessary side-effects for those with COPD (e.g. adrenalsuppression) and/or unnecessary costs (e.g. biologics are veryexpensive).

Alternatively, after a diagnosis of COPD, treatment may includeanti-muscarinic inhalers, antibiotics, lung surgery (e.g. lobectomy), orlung transplantation.

In order to determine whether there were metabolic differences betweenasthma and COPD patients, and to create asthma and COPD metabolicmodels, clinical and urine-based nuclear magnetic resonance spectroscopy(NMR) and mass spectrometry (MS) data can be collected on adults meetingcriteria of asthma and COPD before and after an exacerbation and fromsubjects with stable asthma or COPD. Statistical analysis can beperformed on the NMR or MS data to create models of separation providingunique differences in select metabolites between asthma and COPDsubjects seen in the Emergency Department and in follow-up aftertreatment. In some embodiments the statistical analysis that can beperformed can be partial least squares discriminant analysis (PLS-DA).Using these select metabolomic profiles, the model can correctlydiagnose blinded asthma and COPD subjects with >90% accuracy, showingthat metabolomic analysis of human urine samples can be a usefulclinical tool to differentiate asthma from COPD.

Without any limitation to the foregoing, the present method is furtherdescribed by way of the following examples.

EXAMPLE 1

Patient Characteristics

Asthma or COPD patients with exacerbation: Adults with asthma seen atthe time of an exacerbation through either Location 1 (n=110) orLocation 2 (n=23) were selected based on one or more of the following:a) increasing asthma symptoms (e.g., cough, wheeze, shortness-of-breath,or chest tightness) requiring assessment and a history of similarepisodes; b) bronchodilator response measured by peak expiratory flow(PEF) or significant clinical improvement; and/or c) had a previoushistory of physician-diagnosed asthma. Those from Location 2 also hadpreviously demonstrated >12% reversibility to salbutamol or a positivemethacholine challenge test (PC20 less than 8 mg/ml). Adults with COPDseen in the ED at Location 1 (n=38) were selected based on one or moreof the following: a) increasing symptoms (e.g., cough, wheeze,shortness-of-breath, or chest tightness) requiring assessment and ahistory of similar episodes; b) had a previous history ofphysician-diagnosed COPD. All patients had to present primarily foracute asthma or COPD exacerbation (not a simple prescription refill) andwere excluded if they had acute pneumonia, needed immediateresuscitation (status asthmaticus), had cognitive impairment, or had aknown immunodeficiency. Some subjects consented to return for afollow-up survey and urine collection. Available clinical data are shownin Table 2.

TABLE 2 Characteristics of subjects Alberta Alberta Alberta AlbertaMcMaster Outpatient Outpatient Asthma COPD Asthma Asthma COPD modelmodel Test Set Test Set Test Set (n = 110) (n = 38) (n = 23) (n = 58) (n= 23) Mean Age 32.7 (±11.3) 69.4 (±12.2) 49.9 (±15.7) 33.9 (±13.2) 59.2(±13.1) (in years) (±SD) Female Sex 70 (62%) 23 (52%) 14 (61%) 34 (59%)10 (43%) N (% of the cohort ) Current smoker 32 (28.3%) 19 (43.2%) 0 0 7(32%) N (%) Pack-years of 5 (2, 8) 33.3 (25, 43) unknown 0 (0, 2.75) 30(15, 44) smoking Median (IQR) FEV1 (L) unknown 0.81 (0.58, 1.22) 1.81(1.12, 2.30) 3.31 (2.87, 3.62) 1.96 (1.42, 2.41) Median, (IQR) FEV1 (%)unknown 35.0 (25.0, 50.8) 66.5 (37.0, 82.0) 91.5 (81.8, 98.0) 69.0(54.0, 77.3) Median, (IQR) Inhaled 78 (68) 32 (80) unknown 46 (85) 21(95) Corticosteroid N (%)

Asthma or COPD patients stable in an outpatient setting: Adults withasthma (n=58) or COPD (n=24) were recruited in an outpatient setting atLocation 1. Asthma diagnosis was based on: a) a previous history ofphysician-diagnosed asthma; b) previously demonstrated >12%reversibility to salbutamol or a positive methacholine challenge test(PC20 less than 8 mg/ml); c) not currently smoking; and d) having a <10pack year history of smoking in their past. COPD diagnosis was based onone or more of the following: a) a previous history ofphysician-diagnosed COPD; b) having a post-bronchodilator FEV1/FVC<70%.Patients were excluded if they were taking systemic corticosteroids orprescription medications for diabetes or hypothyroidism. Availableclinical data are shown in Table 2.

Statistical Analysis of Clinical Data

GraphPad prism™ (V.6) was used for statistical analysis of the clinicaldata. A non-parametric t-test was used to compare two group experiments.Repeated Measures ANOVA with Bonferroni's Multiple Comparison Test were129 employed to compare multiple groups. Data are presented as mean±1standard error (SEM) or median with interquaterile range (IQR). A pvalue of <0.05 was considered significant.

Urine Sample Collection and Preparation

A urine sample was collected from each patient and promptly placed in afreezer (−20° C.). Within 3 hours of collection, the urine samples werestored in a −80° C. freezer. Urine samples were thawed only once in abiosafety fume hood and a 630 μl aliquot was removed and placed in a 1.5ml Eppendorf tube followed by the addition of 70 μl of a referencebuffer solution.

NMR Spectral and Statistical Analysis

Quantification of 86 metabolites was performed using the Chenomx NMRSuite Professional software package Version 4.6 (Chenomx Inc., Edmonton,AB). The software contains a database of known metabolites with theirreferenced spectral resonant frequencies or signatures enabling thequalitative and quantitative analysis of metabolites in urine. Toaccount for hydration status of the subjects, metabolites werereferenced to creatinine and the values were log transformed. Partialleast squares discriminate analysis (PLS-DA) was performed (SIMCA-P 11,Umetrics, USA). This process identifies the metabolites whoseconcentrations differed significantly between groups of patients. Insome embodiments, most metabolites do not differ greatly between groupsand including metabolites of low significance can be detrimental tocreating an accurate diagnostic model of separation. Metabolites withconsistently greater difference in concentration between groups aredisplayed by the software as a Co-efficient of Variation (COV) Plot andVariables of Importance Plot (VIP). To choose an accurate list ofmetabolites, metabolites were removed until the model could correctlydiagnose blinded samples not part of the model at a satisfactory level.A false positive rate of 5-10% was set as an acceptable limit. ThePLS-DA based model can then be used to generate a prediction score (0-1)of unclassified, blinded data not part of the model (i.e., scores <0.5would be predicted to be asthma vs. COPD subjects). Thereceiver-operator curve (ROC) was generated to evaluate the relationshipbetween the sensitivity and specificity of the prediction score atvarious cutpoints, and to consider the score's overall function as apredictor.

Baseline Patient Characteristics for the Metabolomic Model of AsthmaVersus COPD

The COPD populations were both significantly older than the asthmagroups, though the asthma group from Location 2 was significantly olderthan the other asthma cohorts in Location 1. The sex difference amongthese groups was similar. Subjects having an asthma exacerbation at theLocation 1 were not excluded if they currently smoked; however, thenumber of pack years of smoking was much lower in the asthma groupcompared to the COPD exacerbation group (a median of 5 pack yearscompared to 33 respectively). None of the subjects recruited with asthmaat Location 2 were current smokers and all had <10 pack years of pastsmoking history. All the COPD subjects used to create the models ofasthma versus COPD in the ED or in follow-up had smoked at one time.Their pack years of smoking were also substantially higher compared tothe asthma groups. There were four COPD subjects in the Location 1 COPDoutpatient test set that denied ever smoking. That being said, therewere also a number of patients with asthma included in the models thatwere current smokers. Regarding lung function data, FEV1 wassignificantly lower in the COPD cohort compared to the blinded asthmacohort from Location 2 (p<0.003). Spirometry data were not availablefrom the asthma model subjects from Location 1. Only the Location 2cohort had skin testing performed; 13 of the 23 subjects (56%) were skintest positive for at least one aeroallergen.

Baseline Patient Characteristics for the Test Set of Outpatient AsthmaVersus COPD

Adults having a diagnosis of asthma or COPD were recruited for urinesampling in an outpatient setting (Table 2). The COPD population wassignificantly older than the asthma group. The sex difference betweenthese groups was similar. Patients with asthma were excluded based onsmoking history as such they had a lower pack year history of smokingcompared to the COPD group. Regarding lung function data, FEV1%predicted and the FEV1/FVC were both significantly lower in the COPDcohort compared to the blinded asthma cohort from Location 2 (p<0.0001each).

Urine Metabolomic Profiling Can Differentiate Asthma Versus COPDExacerbation

The metabolites of asthma and COPD patients were compared. Most of themetabolites excreted in the urine did not differ greatly between groups,and adding metabolites of low importance rendered the PLS-DA based modelless accurate. To remove irrelevant metabolites, urine samples from 19patients with asthma were randomly withheld to be used as a test set.The model used 91 subjects with asthma and 38 with COPD. Using the testset, metabolites that allowed the model to correctly classify theseblinded asthma patients with 95% accuracy were removed (18 of 19 with ablinded PLS-DA prediction score >0.5). The final list of remainingmetabolites used one component consisting of 22 metabolites in the VIPlist giving an R2=0.72, Q2=0.69.

Referring now to FIGS. 1A-1C, an embodiment of a metabolomic model ofexacerbation of asthma versus COPD in the ED is depicted. Urinemetabolite levels were measured in subjects in the ED with either anexacerbation of asthma or COPD. Using a blind test set, PLS-DA analysis(SIMCA P-11) of these metabolites created a model of separation(R2=0.72, Q2=0.69). Illustrated are: FIG. 1A. the Variables ofImportance plot ranking the metabolites according to their significancein the model; FIG. 1B. scaled and centered metabolite differencesbetween groups shown as the Coefficients of Variation plot. Metabolitesthat were higher in COPD than Asthma subjects are shown with bars risingabove zero, while those higher in Asthma than COPD are going below; FIG.1C. the PLS-DA prediction scores for each subject with error barsrepresenting medians and interquartile ranges. The PLS-DA algorithmseparates groups of data based on a score of 0-1; in this case a valueabove 0.5 indicates the subject has asthma while below 0.5 indicatesCOPD.

The importance of the metabolites used for separation of these twogroups is shown as a VIP Plot (FIG. 1A). The differences inconcentration of these metabolites between groups are shown as the COVPlot (FIG. 1B). Metabolites that were higher in COPD than asthmasubjects are shown with bars rising above zero, while those higher inasthma than COPD are going below. Graphic presentation of the quality ofseparation between groups in the model is shown by their respectivePLS-DA scores (FIG. 1C). The final metabolites chosen and theirconcentrations are shown in Table 3.

TABLE 3 The concentration of each metabolite for each subject group isshown as the median and interquartile range (IQR) in mmol ofmetabolite/mmol creatinine (except for creatinine for which actualvalues are shown (mmol)). The metabolites used to discriminate thedifferent groups of subjects are labeled as: (α) required for separationof COPD vs asthma during exacerbation: (β) required for separation ofCOPD vs asthma in follow-up. COPD in ED Asthma ED Medan IQR Median IQR1-Methylnicotinamide (β) 0.0034 0.0010 0.0055 0.0024 0.0010 0.00373-Hydroxyisovalerate (α, β) 0.0050 0.0026 0.0088 0.0066 0.0047 0.0097Arginine (α, β) 0.0070 0.0065 0.0245 0.0189 0.0134 0.0237 Ascorbate (α)0.0005 0.0005 0.0302 0.0005 0.0005 0.0005 Betaine (α, β) 0.0138 0.00560.0278 0.0071 0.0050 0.0105 Choline (α, β) 0.0029 0.0006 0.0047 0.00060.0006 0.0025 Citrate (α) 0.1986 0.0819 0.3327 0.2443 0.1744 0.3673Creatinine (α) 6671 2394 9324 11252 6094 18286 Dimethylamine (α, β)0.0457 0.0395 0.0525 0.0340 0.0300 0.0391 Glucose (α, β) 0.0711 0.03371.6860 0.0334 0.0218 0.0473 Glutamine (α, β) 0.0286 0.0158 0.0466 0.03920.0319 0.0539 Glycine (β) 0.0952 0.0486 0.1594 0.1160 0.0640 0.2006Glycolate (α) 0.0258 0.0176 0.0413 0.0366 0.0258 0.0552 Guanidoacetate(α, β) 0.0091 0.0020 0.0186 0.0166 0.0103 0.0255 Histidine (α, β) 0.03000.0139 0.0484 0.0353 0.0202 0.0549 Hypoxanthine (α, β) 0.0083 0.00350.0131 0.0056 0.0025 0.0091 Isoleucine (α) 0.0017 0.0003 0.0032 0.00090.0003 0.0016 Methanol (α) 0.0029 0.0015 0.0070 0.0016 0.0011 0.0036Pantothenate (α) 0.0050 0.0027 0.0075 0.0030 0.0017 0.0045Phe-Derivative (α, β) 0.1044 0.0754 0.1392 0.0481 0.0279 0.0726Succinate (β) 0.0056 0.0037 0.0112 0.0065 0.0030 0.0117 Taurine (α, β)0.1148 0.0314 0.2133 0.0184 0.0051 0.0517 Uracil (α, β) 0.0010 0.00100.0018 0.0028 0.0010 0.0046 Urea (α) 26.73 17.38 36.81 19.58 14.42 27.06Xylose (α) 0.0239 0.0020 0.0487 0.0080 0.0020 0.0163 COPD ED-followupAsthma ED-followup Median IQR Median IQR 1-Methylnicotinamide (β) 0.00350.0010 0.0071 0.0016 0.0010 0.0034 3-Hydroxyisovalerate (α, β) 0.00340.0017 0.0044 0.0063 0.0051 0.0082 Arginine (α, β) 0.0082 0.0070 0.02340.0190 0.0146 0.0240 Ascorbate (α) 0.0005 0.0005 0.0131 0.0005 0.00050.0005 Betaine (α, β) 0.0095 0.0049 0.0173 0.0057 0.0034 0.0088 Choline(α, β) 0.0024 0.0011 0.0041 0.0013 0.0006 0.0024 Citrate (α) 0.27520.1387 0.4160 0.2417 0.1534 0.4114 Creatinine (α) 5213 2910 7474 104265928 18413 Dimethylamine (α, β) 0.0419 0.0369 0.0464 0.0330 0.02890.0375 Glucose (α, β) 0.0375 0.0291 0.0631 0.0271 0.0223 0.0354Glutamine (α, β) 0.0196 0.0167 0.0309 0.0446 0.0298 0.0548 Glycine (β)0.0759 0.0497 0.1113 0.1041 0.0672 0.2057 Glycolate (α) 0.0210 0.01180.0321 0.0347 0.0235 0.0498 Guanidoacetate (α, β) 0.0143 0.0104 0.02440.0166 0.0092 0.0270 Histidine (α, β) 0.0177 0.0112 0.0257 0.0290 0.01170.0441 Hypoxanthine (α, β) 0.0060 0.0009 0.0076 0.0036 0.0009 0.0070Isoleucine (α) 0.0013 0.0003 0.0020 0.0006 0.0003 0.0015 Methanol (α)0.0054 0.0024 0.0072 0.0021 0.0010 0.0039 Pantothenate (α) 0.0024 0.00160.0081 0.0026 0.0018 0.0035 Phe-Derivative (α, β) 0.0948 0.0722 0.14420.0538 0.0290 0.0942 Succinate (β) 0.0060 0.0027 0.0111 0.0067 0.00420.0151 Taurine (α, β) 0.0568 0.0219 0.1045 0.0078 0.0042 0.0141 Uracil(α, β) 0.0010 0.0010 0.0039 0.0029 0.0010 0.0055 Urea (α) 27.56 19.1539.22 19.94 14.34 28.37 Xylose (α) 0.0020 0.0020 0.0151 0.0072 0.00200.0142

Validity Assessment (Asthma vs COPD During Exacerbation)

To validate the proposed metabolomic model and its applicability as adiagnostic tool for asthma in the ED setting, the concentrations ofmetabolites from adults (n=23) with asthma exacerbation from Location 2were entered. The urine metabolite values for these patients wereentered into the PLS-DA model without a diagnosis, thus the computer wasblinded for the PLS-DA derived model of asthma versus COPD. Theindividual PLS-DA prediction scores are shown in FIG. 1C. A thresholdPLS-DA score of >0.5 was used to designate asthma. The model was able tocorrectly diagnose the blinded asthma samples in 20 of 23 samples (87%accuracy).

Urine Metabolomic Profiling Can Differentiate Asthma Versus COPD inFollow-Up After an Exacerbation

To determine if this metabolomic approach could be used to differentiateasthma and COPD in the outpatient setting, urine samples were studiedfrom the asthma or COPD subjects 1-2 weeks post-exacerbation (Table 2).This follow-up data were also obtained to determine a measure ofreproducibility for this metabolomic approach (i.e., if these data weresimply dependent on confounders such as time of day or diet). 61 asthmaand 23 COPD subjects were used to create a follow-up model. The samereturning asthma subjects (now n=16) were withheld as a test set toremove metabolites of low importance.

Referring now to FIGS. 2A-2C, an embodiment of a metabolomic model ofsubjects in follow-up post-exacerbation of asthma versus COPD isdepicted. Urine metabolite levels can be measured from subjects infollow-up from an ED visit for either asthma or COPD. Using a blind testset, PLS-DA analysis (SIMCA P-11) on these metabolite levels created amodel of separation (R2=0.78, Q2=0.70). Illustrated are: FIG. 2A. theVariables of Importance plot ranking the metabolites according to theirsignificance in the model; FIG. 2B. scaled and centered metabolitedifferences between groups shown as the Coefficients of Variation plot;FIG. 2C. the PLS-DA prediction scores for each subject with error barsrepresenting medians and interquartile ranges. The PLS-DA algorithmseparates groups of data based on a score of 0-1; in this case a valueabove 0.5 indicates the subject has asthma while below 0.5 indicatesCOPD.

The metabolomic model can correctly classify a blinded asthma test setwith 94% accuracy (16/17). The final list of metabolites used toseparate post-exacerbation asthma versus COPD used two componentsconsisting of 16 metabolites in the VIP list producing an R2=0.76,Q2=0.70 (VIP, FIG. 2A). Most of the metabolites used for this follow-upvisit model were the same as those used for the first visit model andthe direction of difference of each metabolite between groups (i.e.,greater or lesser) was the same (see COV plot 2B). Graphic presentationof the quality of separation between groups in the model is shown bytheir respective PLS-DA scores (FIG. 2C). The final list of metaboliteschosen and their concentrations are shown in Table 3.

Validity Assessment of This Model Studying Post-Exacerbation

To validate the metabolomic model of asthma versus COPDpost-exacerbation, the concentrations of metabolites from Location 2(n=21) were entered. These urine metabolite values were entered blindlyfor the PLS-DA model to predict a diagnosis. The individual PLS-DAprediction scores are shown in FIG. 2C with error bars representingmedians and interquartile ranges. The model was able to correctlydiagnose 19 of 21 blinded asthma samples (90% accuracy).

Validity Assessment of This Model Studying Stable Asthma Versus COPD inan Outpatient Setting

To better validate the proposed metabolomic model of asthma versus COPD,the concentrations of metabolites from another cohort of adults withasthma or COPD were entered. These subjects were recruited in anoutpatient setting and not specifically post-exacerbation. This is toshow if the model would still perceive similar metabolomic differencesin what should be a somewhat healthier group not recently recoveringfrom an exacerbation.

Referring now to FIGS. 3A-3B, an embodiment of a metabolomic modelcorrectly predicting asthma versus COPD in blinded outpatients isdepicted. To further determine diagnostic accuracy, the metabolomicmodel of asthma versus COPD post-exacerbation was used to study newsubjects with asthma or COPD in an outpatient setting. Illustrated are:FIG. 3A. the PLS-DA prediction scores for each subject with error barsrepresenting medians and interquartile ranges. The PLS-DA algorithmseparates groups of data based on a score of 0-1; in this case a valueabove 0.5 indicates the subject has asthma while below 0.5 indicatesCOPD; FIG. 3B. Receiver-operator curve for the prediction of asthma vsCOPD. Area under the curve=0.937.

The individual PLS-DA prediction scores are shown in FIG. 3A with errorbars representing medians and interquartile ranges. In some embodiments,the model can have a sensitivity of 92.6% (95% Cl=82.1, 97.9) and aspecificity of 79.2% (95% Cl=57.8, 92.9). The positive predictive value(PPV) for asthma (if prediction score>0.5) was 90.9% (95% Cl=80.0,97.0*) and the negative predictive value (NPV), indicating COPD ratherthan asthma, (prediction score≤0.5) was 82.6% (95% Cl=61.2, 95.0). TheROC curve is shown (FIG. 3B, AUC=0.937).

In current practices, some patients with asthma can be difficult todifferentiate from COPD if they are current smokers. Smokers were notexcluded from the asthma cohort in the ED, since patients with asthmastill continue to smoke. There were 32 asthma patients (28.3%) includedin the models that were current smokers. Despite this, the models sawthe nonsmoking asthma patients in the test sets correctly as asthma. Assuch, smoking was not the major factor in the metabolomicdifferentiation of asthma and COPD of the present disclosure.

Even with optimal clinical phenotyping using current methods, someasthma may mimic the lung function changes typically seen in COPD.Standard clinical criteria, cigarette consumption limits, and FEV1/FVCratios were used in an effort to accurately classify the groups. Despitethis, some of the follow-up asthma exacerbation subjects had fixedobstruction even three weeks post-exacerbation. They were left in thecategory of asthma based on prior spirometry showing reversibility orhyperresponsiveness. The fact that the metabolome model still identifiesthem as asthma despite this fixed obstruction makes the current resultseven more compelling. This would suggest that a subject may have aunique metabolomic phenotype that transcends this physiologicpresentation of fixed obstruction. This approach might also help todefine COPD and asthma overlap syndromes.

While this study was not designed to mechanistically confirm specificmetabolic pathways, there is a sound line of reasoning from which theresults of the methods and uses herein can be inferred from a factualbasis.

EXAMPLE 3 Procedure for Derivatization Using Dansyl Chloride

This process was used in this example to quantify 19 metabolitescontaining phenol or amine functional groups, however, it may be used toquantify any number of metabolites with these functional groups.

Sample Preparation

The initial stock solution contained metabolites with concentrationsranging from 12.5-160 ng/ml. The ¹²C-derivatized stock solution isprepared by mixing 50 μl aliquots of the standard working solution with30 μl bicarbonate/carbonate buffer (pH 9.4, 0.5 M) and 40 μl ¹²C-DNS-Cl(10.125 mg/ml in Acetonitrile (ACN)). The mixture is vortexed for 10sec, spun down and placed in a thermostatically controlled water bath at60° C. for 30 min. Excess DNS-Cl is quenched via the addition of 10 μl0.25 M NaOH with further heating at 60° C. for 10 min. Fifty μl of 425mM formic acid (FA) in 50% ACN is added to acidify the medium and thevolume is completed to 200 μl with 50% ACN. For the preparation of¹³C-Internal standards (¹³C-IS), the aforementioned protocol is followedwith the exception of the use of ¹³C-DNS-Cl for derivatization. Thereaction mixture is finally completed to 250 μl with 50% ACN.

Calibration standards and quality control samples are prepared byspiking proper volumes from ¹²C derivatized stock solution into 50 μl ofblank surrogate matrix spiked with 10 μl ¹³C-IS. The volumes arecompleted to 100 μl with 50% ACN before being transferred to HPLC vialsfor analysis.

Urine Sample Preparation

Individual urine samples are two-fold diluted with ACN, vortexed andcentrifuged at 13,000 rpm for 10 min. Fifty μl from the supernatant aremixed with 30 μl bicarbonate/carbonate buffer (0.5 M, pH 9.4) and 40 μl¹²C-DNS-Cl (10.125 mg/ml). The mixture is vortexed, spun down and placedin a 60° C. thermostatically controlled water bath for 30 min. Ten μl0.25 M NaOH are added and the mixture is placed in the water bath for anadditional 10 min period. The mixture is then acidified with 50 μl 425mM FA in 50% ACN and the volume is completed to 200 μl with 50% ACN.Fifty microliters of this reaction mixture is spiked with 10 μl ¹³C-ISand the volume is completed to 100 μl with 50% ACN before beingtransferred to an HPLC vial for analysis.

For highly abundant metabolites (e.g. glycine, alanine, histidine andglutamine), derivatized urine samples are appropriately diluted withblank surrogate matrix to a volume of 50 μl. The solution is spiked with10 μl 13C2-IS and the volume is completed to 100 μl with 50% ACN.

Blank surrogate matrix is prepared from a pooled urine sample collectedfrom 61 subjects. Derivatization reaction for blank surrogate matrix isprocessed in the same manner as patient samples with the exception ofthe use of ACN in place of the derivatizing reagent. Blank surrogatematrix is used as a diluting medium in the preparation of calibrationstandards and quality control samples. It is also used for dilutingpatient samples whenever needed.

LC-MS/MS Analysis

Liquid chromatography was performed on a 1200 Agilent HPLC system(Mississauga, ON, Canada) interfaced to an AB Sciex 4000 API QTRAPinstrument (AB Sciex, Concord, ON, Canada). Five microliter samplealiquots are injected into the system using a 1200 Agilent autosamplerset at 4° C. Chromatographic separation is achieved on a Kinetex C18Column (100 mm×2.1 mm, 5 μm ID, 100 Å pore size, phenominex, Canada),maintained at 22° C. A binary gradient mobile phase system flowing at0.25 ml/min is employed using (A) 0.1% formic acid in 5% acetonitrile(ACN) and (B) 0.1% formic acid in ACN. Elution conditions are optimizedas follows: t=0 min; 90% A, t=6 min; 70% A, t=19 min; 35% A, t=23 min;1% A, t=24 min; 1% A, t=24.1 min; 90% A, t=33 min; 90% A.

Quantification is achieved in the MRM scan mode using electrosprayionization in the positive ion mode. The monitored precursor ion→production transitions for each ¹²C-analyte (dansyl derivatized metabolite),are m/z [M+H]+→m/z 170.10, with the exception of sarcosine that ismonitored at m/z [M+H]+→m/z 157.10, while the analogues ¹³C-internalstandards (IS) are monitored at m/z [M+H]+→m/z 172.10. A qualifierdiagnostic fragment ion is also monitored for each ¹²C-analyte toconfirm its identity. The Turbo spray ion source temperature is set at550° C. and the ion spray voltage is at 5.5 kV. Curtain gas (CUR) is setat 30, collision gas (CAD) at 6, nebulizer gas (GS1) at 50 and heatergas (GS2) at 50. The entrance potential (EP) and collision exitpotential (CXP) for all transitions are fixed at 10 and 12,respectively. The collision energy (CE) and declustering potential (DP)are optimized for each 12C2-analyte individually. The dwell time is 20msec for each transition at unit resolution with 1.4504 sec cycle timeand 5.007 msec pause between mass ranges. Data processing is achieved onAnalyst 1.6 software (Applied biosystems).

EXAMPLE 4 Procedure for Derivatization Using DmPA

This process was used in this example to quantify 17 metabolitescontaining carboxylic acid functional groups, however it may be used toquantify any number of metabolites with this functional group.

Sample Preparation

For ¹²C-DmPA labeling, a 100 μL of a standard or urine sample is mixedwith a 100 μL of TEA (20 μL/mL in ACN). The mixture is vortexed and spundown. Then a 100 μL of ¹²C-DmPA (30 mg/mL in ACN) is added, vortexed,and spun down. The resulting mixture is heated in a water bath at 85.0for 30 minutes. After 30 minutes, the derivatized solution is cooleddown to room temperature and diluted by mixing 300 μL of this solutionwith 300 μL of formic acid solution (20 μL/mL in water). Then, 60 μL ofthe latter solution is mixed with solvent mix I (for calibration curvesamples) or solvent mix II (for the urine samples). The solvent mix I isprepared by mixing 120 μL of diluted urine sample (outlined below) with80 μL of internal standard solution (¹³C-DmPA standard solution). Forthe solvent mix II, the diluted urine sample is replaced with 80% ACN.The addition of the formic acid solution and the solvent mixtures (I orII) to the derivatized solution is important for the following reasons:i) terminate the derivatization reaction, ii) enhance thechromatographic peak shape, iii) provide the internal standard, and iv)compensate for the matrix effect of urine. The formic acid terminatesthe derivatization reaction by changing the medium pH into acidic. Thewater enhances the peak shape in the LC-MS chromatograms by increasingthe water content in the injected samples. The diluted urine sample insolvent mix I compensates the matrix effect of the urine in thecalibration curve samples. Finally, all samples are subjected toLC-MS/MS analysis.

For ¹³C-DmPA labeling, a 100 μL of a standard solution is mixed with a100 μL of TEA (20 μL/mL in ACN). The mixture is vortexed and spun down.Then a 100 μL of ¹³C-DmPA (30 mg/mL in ACN) is added, vortexed, and spundown. The resulting mixture is heated in a water bath at 85.0 for 30minutes. The resulting solution will be used as an internal standardsolution.

Urine Sample Preparation

For the analysis, the urine samples are thawed only once and centrifugedat 14000 rpm for 10 minutes. Then the urine supernatant is 1:4 dilutedwith ACN and vortexed to be ready for 12C2-DmPA labeling (referred asthe diluted urine sample or surrogate urine).

LC-MS/MS Analysis

ESI-MS/MS analysis of the 17 organic acid standards is conducted on theAB SCIEX 6500 QTRAP® instrument, which is a hybrid triplequadrupole-linear ion trap (QqLIT) mass spectrometer. The MS/MS analysisis performed in the positive ion mode with an ionspray voltage of 5500V, temperature at 550° C., a declustering potential of 60 V, andcollision energy of 20-40 V using nitrogen as a collision gas. Allparameters are optimized to ensure the formation of the product ionswhile maintaining the presence of the precursor ion. In the LC/multiplereaction monitoring (MRM)-MS, two selective transitions are chosen foreach compound for identification and quantification. The liquidchromatography is performed on the Agilent 1290 Series UPLC System usinga Kinetex C18 column (5 μm×50 mm, 2.1 ID) and a gradient system of twosolvents. Solvent A consists of 5 mM ammonium formate, 5% ACN, and 0.1%formic acid. Solvent B consists of 60% ACN, 40% methanol, and 0.1%formic acid. The gradient system started with 95% of solvent A andgradually decreased to 80% over 3 minutes then decreased to 75% over 9min, followed by another decrease to 20% over 5 min. At the minute 17 ofthe run time, solvent A started to increase from 20% to 95% over 3minutes. Finally, solvent A continued at 95% for 10 minutes toequilibrate the column for the next injection. Flow rate is set at 0.25mL/min and sample injection at 5 μL. The autosampler is set at 4° C. andthe column is maintained at 20° C.

With regards to normalization of hydration status and validation, forboth Examples 3 and 4 creatinine is used for data normalization to thehydration status of the subject. It is determined using the QuantiChrom™Creatinine Assay kit (BioAssay Systems, Hayward, Calif. 94545, U.S.A.).The quantified metabolites are each normalized to the respectivecreatinine value in each urine sample. As well, each method was fullyvalidated according to the FDA in terms of precision, accuracy,selectivity, carry over, linear range, dilution integrity, stability andmatrix effects.

EXAMPLE 5

Additional patient samples with COPD (n=15) or age matched asthma (n=30)were analyzed. Similar criteria for diagnosis as were used in Examples 1and 2 were also used in this example. Using the MS methods of Examples 3and 4, 36 metabolites were quantified for each subject. Using PLSDA ofMS data, metabolomic analysis of urine was able to differentiate asthmafrom COPD subjects, as is depicted in FIG. 6. FIGS. 7A-7B show the COVand VIP. Considering the small sample size this separation is quitegood.

The differences in metabolite seen in the NMR data are also similarlydifferent using the MS methods, as can be seen in FIGS. 7A-7B. Thedegree of difference may not be as great compared to the NMR data, butthis is likely due to the smaller sample size of subjects in thisexample. With larger sample sizes similar statistical strength isexpected. In addition, we see new metabolites of importance not seenwith NMR. For example, alanine, valine, 1-methylhistamine, tyrosine,asparagine, and ethanolamine are metabolites depicted in FIGS. 7A-7Bthat show differences between subject groups. Thus, targetingmetabolites with MS gives additional strength to the diagnosis of asthmaversus COPD.

Overall, physicians require better objective tests of asthma and COPD.The present methods and models can lead to improved diagnosticcapabilities. The metabolites in Table 4 are likely to be relevant inany metabolomic model which is designed to diagnosis asthma versus COPD.These individual metabolites were identified either by NMR, MS or both.While specific numbers of metabolites were used in the Examples above,it is anticipated that at least 11 of metabolites would be sufficient todifferentiate between asthma and COPD, as outlined above. In a preferredembodiment at least the following metabolites would be measured andincluded in the statistical analysis; 3-Hydroxyisovalerate; Glutamine;Arginine; Lactic acid; Glycolate; Tyrosine; 2-oxoglutarate; Glycine;Histidine; 1-methylhistamine; and Taurine.

TABLE 4 Metabolites identified by MS, NMR and both Identified by MS &Identified by MS NMR Identified by NMR 1-methyl histamine 3-hydroxyisovaleric 1 acid methylnicotinamide 2-oxoglutaric acid Betaineascorbate 2-hydroxy isobutyric acid Creatinine choline 3 Hydroxy 3Methyl glutaric Glutamine citrate acid 3-hydroxy butyric acid Glycinedimethylamine Alanine Glycolic acid glucose Asparagine Histidineguanidoacetate Cis/Trans aconitic acids Isoleucine hypoxanthineethanolamine Pantothenic acid methanol Lactic acid Succinic acidphe-derivative Lysine Taurine uracil O-acetylcarnitine arginine ureaPyroglutamic acid xylose Sarcosine Serine Threonine Tryptophane tyrosineValine

The scope of the claims should not be limited by the embodiments as setforth in the examples herein, but should be given the broadestinterpretation consistent with the description as a whole.

Although a few embodiments have been shown and described, it will beappreciated by those skilled in the art that various changes andmodifications can be made to the embodiments described herein. The termsand expressions used in the above description have been used herein asterms of description and not of limitation, and there is no intention inthe use of such terms and expressions of excluding equivalents of thefeatures shown and described or portions thereof, it being recognizedthat the invention is defined and limited only by the claims thatfollow.

The teachings provided herein can be applied to other methods, notnecessarily the method described herein. The elements and acts of thevarious embodiments described above can be combined to provide furtherembodiments.

These and other changes can be made to the invention in light of theabove description. While the above description details certainembodiments of the invention and describes certain embodiments, nomatter how detailed the above appears in text, the invention can bepracticed in many ways. Details of the method may vary considerably intheir implementation details, while still being encompassed by theinvention disclosed herein.

Particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific embodimentsdisclosed in the specification. Accordingly, the actual scope of theinvention encompasses not only the disclosed embodiments, but also allequivalent ways of practicing or implementing the invention.

The above description of the embodiments of the invention is notintended to be exhaustive or to limit the invention to the precise formdisclosed above or to the particular field of usage mentioned in thisdisclosure. While specific embodiments of, and examples for, theinvention are described above for illustrative purposes, variousequivalent modifications are possible within the scope of the invention,as those skilled in the relevant art will recognize. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

While certain aspects of the invention are presented below in certainclaim forms, the inventor contemplates the various aspects of theinvention in any number of claim forms. Accordingly, the inventorreserves the right to add additional claims after filing the applicationto pursue such additional claim forms for other aspects of theinvention.

REFERENCES

The following references are hereby incorporated by reference into thisapplication in their entirety.

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We claim:
 1. A method of differentiating asthma versus ChronicObstructive Pulmonary Disease (COPD) in a subject, comprising: measuringa concentration of at least 11 metabolites comprising3-Hydroxyisovalerate, Glutamine, Arginine, Lactic acid, Glycolate,Tyrosine, 2-oxoglutarate, Glycine, Histidine, 1-methylhistamine andTaurine in a urine sample obtained from the subject to determine asubject profile; comparing the subject profile, by a statisticalanalysis, to a predetermined asthma disease state profile and to apredetermined COPD state profile; identifying that the subject hasasthma or COPD when the statistical analysis identifies the subjectprofile as more similar to one of the predetermined disease stateprofiles than to the other predetermined disease state profile; whereinthe concentration of the Glutamine, Arginine, Tyrosine, Glycine,Histidine, 1-methylhistamine, and Taurine is measured by massspectrometry of a first derivatized sample prepared by dansyl chloride(DNS-Cl) derivatization of the urine sample; and wherein theconcentration of the 3-Hydroxyisovalerate, Lactic Acid, Glycolate, and2-oxoglutarate is measured by mass spectrometry of a second derivatizedsample prepared by dimethylaminophenacyl (DmPA) derivatization of theurine sample.
 2. The method of claim 1, wherein the concentration ofeach of the at least 11 metabolites is normalized against a measuredconcentration of creatinine in the urine sample.
 3. The method of claim1, wherein the DNS-Cl derivatization of the urine sample comprises:combining a first portion of the urine sample with a buffer and¹²C-DNS-Cl; heating the combined sample; adding NaOH to the heatedsample to quench excess DNS-Cl; further heating the quenched sample; andacidifying the further heated sample with formic acid.
 4. The method ofclaim 3, wherein DNS-Cl derivatization of the urine sample furthercomprises spiking the acidified sample with an ¹³C-DNS-Cl internalstandard.
 5. The method of claim 1, wherein DmPA derivatization of theurine sample comprises: combining a second portion of the urine samplewith triethanolamine (TEA) and ¹²C-DmPA; heating the combined sample;cooling the heated sample; and acidifying the cooled sample with formicacid.
 6. The method of claim 5, wherein DmPA derivatization of the urinesample further comprises spiking the acidified sample with a ¹³C-DmPAinternal standard solution.
 7. The method of claim 1, wherein thestatistical analysis is a partial least squares discriminant analysis.8. The method of claim 1, wherein the at least 11 metabolites furthercomprise at least one of Pantothenic acid, Cis/Trans aconitic acids, 3Hydroxy 3 Methyl glutaric acid, Ethanolamine, Pyroglutamic acid,Tryptophane, Creatinine, Valine, 2-hydroxy isobutyric acid, Lysine,O-acetylcarnitine, Serine, Alanine, Isoleucine, 3-methyl adipic acid,3-hydroxy butyric acid, Succinic acid, Sarcosine, Betaine, andThreonine.
 9. The method of claim 8, wherein the concentration of atleast one of Ethanolamine, Tryptophane, Creatinine, Valine, Lysine,Serine, Alanine, Isoleucine, Sarcosine, and Threonine is measured bymass spectrometry of the first derivatized sample.
 10. The method ofclaim 8, wherein the concentration of at least one of Pantothenic acid,Cis/Trans aconitic acids, 3 Hydroxy 3 Methyl glutaric acid, Pyroglutamicacid, 2-hydroxy isobutyric acid, O-acetylcarnitine, 3-methyl adipicacid, 3-hydroxy butyric acid, Succinic acid, and Betaine is measured bymass spectrometry of the second derivatized sample.
 11. A method foranalyzing a urine sample from a subject to obtain a subject profile, themethod comprising: derivatizing a first portion of the urine sample withdansyl chloride (DNS-Cl) to produce a first derivatized sample;analyzing the first derivatized sample by mass spectrometry to measure aconcentration of at least one first metabolite with a phenol or aminefunctional group; derivatizing a second portion of the urine sample withdimethylaminophenacyl (DmPA) to produce a second derivatized sample;analyzing the second derivatized sample by mass spectrometry to measurea concentration of at least one second metabolite with a carboxylic acidfunctional group; obtaining the subject profile based on theconcentrations of the at least one first metabolite and the at least onesecond metabolite; comparing the subject profile, by a statisticalanalysis, to a predetermined asthma disease state profile and to apredetermined Chronic Obstructive Pulmonary Disease (COPD) disease stateprofile; and identifying if the subject has asthma or COPD when thestatistical analysis identifies the subject profile as more similar toone of the predetermined disease state profiles than to the otherpredetermined disease state profile; and wherein the at least one firstmetabolite comprises Glutamine, Arginine, Tyrosine, Glycine, Histidine,1-methylhistamine, and Taurine and the at least one second metabolitecomprises 3-Hydroxyisovalerate, Lactic Acid, Glycolate, and2-oxoglutarate.
 12. The method of claim 11, further comprisingnormalizing the concentrations of the at least one first and secondmetabolite using a measured concentration of creatinine in the urinesample.
 13. The method of claim 11, wherein derivatizing the firstportion of the urine sample with DNS-Cl comprises: combining the firstportion of the urine sample with a buffer and ¹²C-DNS-Cl; heating thecombined sample; adding NaOH to the heated sample to quench excessDNS-Cl; further heating the quenched sample; and acidifying the furtherheated sample with formic acid.
 14. The method of claim 13, whereinderivatizing the first portion of the urine sample with DNS-Cl furthercomprises spiking the acidified sample with a ¹³C-DNS-Cl internalstandard.
 15. The method of claim 11, wherein derivatizing the secondportion of the urine sample with DmPa comprises: combining the secondportion of the urine sample with triethanolamine (TEA) and ¹²C-DmPA;heating the combined sample; cooling the heated sample; and acidifyingthe cooled sample with formic acid.
 16. The method of claim 15, whereinderivatizing the second portion of the urine sample further comprisesspiking the acidified sample with a ¹³C-DmPA internal standard solution.17. The method of claim 11, wherein the at least one first metabolitefurther comprises at least one of Ethanolamine, Tryptophane, Creatinine,Valine, Lysine, Serine, Alanine, Isoleucine, Sarcosine, and Threonine.18. The method of claim 11, wherein the at least one second metabolitefurther comprises at least one of Pantothenic acid, Cis/Trans aconiticacids, 3 Hydroxy 3 Methyl glutaric acid, Pyroglutamic acid, 2-hydroxyisobutyric acid, O-acetylcarnitine, 3-methyl adipic acid, 3-hydroxybutyric acid, Succinic acid, and Betaine.
 19. The method of claim 11,wherein the statistical analysis is a partial least squares discriminantanalysis.